Methods used to make predictions and generalizations about a population based on a sample, such as hypothesis testing and confidence intervals.
Probability: Understanding the likelihood of an event occurring.
Sampling: How to select a representative subset from a larger population.
Central limit theorem: The distribution of sample means approaches a normal distribution.
Hypothesis testing: Making decisions based on data and probability.
Confidence intervals: Estimating a parameter with a range of values.
Type I and Type II errors: The risks of making a wrong decision in hypothesis testing.
p-values: Determining the statistical significance of results.
Correlation and regression: Measuring the relationship between variables.
ANOVA: Analyzing variance between groups.
Chi-square test: Analyzing independence and association in categorical data.
Non-parametric methods: Alternatives to traditional inferential statistics.
Power and sample size: Determining the minimum sample size needed for a study.
Bayesian statistics: Incorporating prior knowledge and updating beliefs based on new data.
Hypothesis Testing: It’s a process of selecting a sample from a population for experiment purpose to confirm or reject the hypothesis to estimate the behavior of the entire population.
Confidence Intervals: It's the degree of accuracy in the estimation of the population value that is calculated from the collected sample data. It shows a range of values within which the population parameters can be defined.
Regression Analysis: It's a statistical method that determines the relationship between dependent and independent variables. It shows how much one or more independent variables affect the dependent variable.
Analysis of Variance (ANOVA): ANOVA is a statistical method that examines the variance between multiple groups or samples. It's a hypothesis test which measures the variation between means of two or more groups.
t-test: It's a statistical tool to compare the means of the two independent groups to determine the difference between them. Two popular t-tests are ANOVA and paired t-tests.
Chi-square test: It is most commonly used to test for independence of categorical variables on which there are two or more independent groups.
Correlation Analysis: It measures the degree of association or relationship between two continuous variables by calculating the correlation coefficient. The correlation coefficient ranges between -1 to +1, where -1 means a strong negative relationship and +1 means a strong positive relationship.
Time Series Analysis: It’s a statistical method to study on any data or value change over time. Time series analysis is often used in forecasting and trend analysis.
Survival Analysis: It's a statistical tool to analyze the survival rate, which is a commonly used statistic in medicine or life science.
Bayesian Statistics: It's used to represent a hypothesis as a probability distribution rather than a single point estimate. It helps to make more informed decisions based on prior knowledge and present data. The Bayesian approach is very useful when the sample size is very small, or there is a significant amount of uncertainty.